Campus Intelligence Mental Health Searching System Based on Face Recognition Technology

Aiming at the problem that the existing auxiliary diagnosis methods for fractures are mostly limited to specific body parts and lack generality and robustness when applied to multi-part diagnoses, this study proposes a two-stage upper limb fracture auxiliary diagnosis method based on deep learning and develops a corresponding auxiliary diagnosis system. In the first stage, this study employs an improved ResNet-50 model combined with transfer learning and a Squeeze-and-Excitation (SE) attention mechanism for fracture image localization. In the second stage, an improved You Only Look Once (YOLO) model based on Scale Sequence Feature Fusion (SSFF) and Triple Feature Encoder (TFE) modules is used for fracture diagnoses in different body parts. Contrary to the traditional methods that are tailored to specific body parts, the integrated design approach presented in this paper is better suited to meeting the diagnostic needs of multiple body parts, demonstrating better generality and clinical application potential.

Electret-based flexible pressure sensor for respiratory diseases auxiliary diagnosis system using machine learning technique

Objective: To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology. Methods: This was a retrospectively study. Newborn screening data (n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data (n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results: A total of 3 665 697 newborns' screening data were collected including 3 019 cases' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment (n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion: An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

Background The Recreovía (PR) program promotes healthy habits and lifestyles through the practice of physical activity (PA) directed and musicalized in public spaces in Bogotá Colombia, with an average annual attendance of 800,000 participants. The objective of this study was to evaluate the changes in Emotional Facial Expression (EFE) during a PA session of the PR. The emotions evaluated were happiness, sadness, surprise, scare, disgust, and contempt, in addition, the neutral emotional state described by Ekman (1972). Methods Descriptive cross-sectional study, with 566 subjects aged 15 to 85 years (72.1% women and 27.9% men). The sample was estimated with a 95% confidence interval and a 5% margin of error. The facial expression analysis was performed with the FaceReader® facial recognition software and equipment. The data were analyzed through the techniques of Mann-Whitney U and Kruskal-Wallis with SPSS version 24 software. Results The age ranges were distributed as follows: 15-30 (22.8%), 31-45 (49.6%), 46-60 (26.9%) and 61-85 (0.7%). Users placed their emotions like this, neutral 47%, happiness 20%, sadness 7%, angry 6%, surprise 3%, scare 4%, disgust 8% and contempt 1%. The differences by gender were found, sadness (women 8%, men 5%, p = 0.007) and angry (women 5%, men 7%, p = 0.040). In the comparison by age, differences were found between 15-30 years and 46-60 years (neutral 35%, 47%, p = 0.000; happiness 27%, 13% p = 0.001; scare 2%, 6% p = 0.016). Conclusions Neutrality is an emotional state that does not show happiness or sadness, it is hypothesized that during this time the learning processes predominate. The most evoked emotion during the AF session was happiness followed by far by sadness, emotion related to the ambiguities of the movements in the process of learning motor gestures and happiness corresponds to the achievement in the execution that generates adherence to the program Recreovía that contributes to the mental and social health of the participants. Key messages Reduced levels of physical activity have a great impact on health. A sedentary lifestyle and emotional expressions is a more common risk factor that affects people's mental health. Mental disorders are a multifactorial condition that affects health, and in this case, physical activity for Bogotanos becomes a vehicle that transforms people's health and quality of life.

BackgroundAdolescence is characterised by profound changes in physical, cognitive, emotional and social functioning and is associated with psychological distress and suicidal tendencies. The family plays a significant role in safeguarding and promoting adolescents’ mental health. However, little is known about the influence of family functioning on adolescents’ psychological distress and suicidal tendencies in Ghana.AimThis study examined the association between family functioning, psychological distress and suicidal tendencies among adolescents in Ghana.SettingThe study was conducted at three public senior high schools in Accra, Ghana.MethodsThis cross-sectional study randomly recruited 600 students. We analysed the data using Spearman’s rho correlation and simultaneous multiple logistic regression.ResultsThe study revealed that poor family problem-solving had no statistically significant relationship with adolescents’ psychological distress and suicidal tendencies. After covarying by gender and age, poor family communication was not statistically and significantly associated with adolescent psychological distress but had a statistically significant positive association with suicidal tendencies. However, and more importantly, poor family affective involvement predicted both adolescents’ psychological distress and suicidal tendencies.ConclusionThese findings suggest the key role of the family in adolescents’ psychological distress and suicidal tendencies, emphasising the need for tailored family psychological interventions in Ghana.ContributionThis study contributes to the emerging literature on family functioning and its association with adolescent psychological distress and suicidal tendencies, underpinned by the McMaster model of family functioning (MMFF) in the Ghanaian setting.

Different styles of social interaction are one of the core characteristics of autism spectrum disorder (ASD). Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition (FER) in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in ASD. Then, we discuss the dual problem of FER – namely facial emotion expression (FEE) or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy (FERTH)] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: (1) a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or (2) teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area.

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

IntroductionThe research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques.MethodsHere, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor’s diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis.ResultsIt achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria.DiscussionThe auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

Facial expression recognition impairment has been reported in Parkinson's disease. While some authors have referred to specific emotional disabilities, others view them as secondary to executive deficits frequently described in the disease, such as working memory. The present study aims to analyze the relationship between working memory and facial expression recognition abilities in Parkinson's disease. We observed 50 patients with Parkinson's disease and 49 healthy controls by means of an n-back procedure with four types of stimuli: emotional facial expressions, gender, spatial locations, and non-sense syllables. Other executive and visuospatial neuropsychological tests were also administered. Results showed that Parkinson's disease patients with high levels of disability performed worse than healthy individuals on the emotional facial expression and spatial location tasks. Moreover, spatial location task performance was correlated with executive neuropsychological scores, but emotional facial expression was not. Thus, working memory seems to be altered in Parkinson's disease, particularly in tasks that involve the appreciation of spatial relationships in stimuli. Additionally, non-executive, facial emotional recognition difficulty seems to be present and related to disease progression.

Objective. The measurement of electromyography (EMG) signals with needle electrodes is widely used in clinical settings for diagnosing neuromuscular diseases. Patients experience pain during needle EMG testing. It is significant to develop alternative diagnostic modalities.Approach. This paper proposes a portable magnetomyography (MMG) measurement system for neuromuscular disease auxiliary diagnosis. Firstly, the design and operating principle of the system are introduced. The feasibility of using the system for auxiliary diagnosis of neuromuscular diseases is then studied. The magnetic signals and needle EMG signals of thirty subjects were collected and compared.Main results. It is found that the amplitude of muscle magnetic field signal increases during mild muscle contraction, and the signal magnitudes of the patients are smaller than those of normal subjects. The diseased muscles tested in the experiment can be distinguished from the normal muscles based on the signal amplitude, using a threshold value of 6 pT. The MMG diagnosis results align well with the needle EMG diagnosis. In addition, the MMG measurement indicates that there is a persistence of spontaneous activity in the diseased muscle.Significance.The experimental results demonstrate that it is feasible to auxiliary diagnose neuromuscular diseases using the portable MMG system, which offers the advantages of non-contact and painless measurements. After more in-depth, systematic, and quantitative research, the portable MMG could potentially be used for auxiliary diagnosis of neuromuscular diseases. The clinical trial registration number is ChiCTR2200067116.

Mental health issues have become increasingly prevalent in modern society, driving the advancement of emotion recognition technologies for psychological interventions. This study investigates non-contact health monitoring and emotion recognition technology for auxiliary detection, diagnosis, and treatment of mental disorders including depression, anxiety, and autism spectrum disorders. By analyzing physiological signals and behavioral data, this technology provides objective evidence for clinical decision-making. Physiological signals such as EEG and ECG are difficult to disguise, and deep learning algorithms significantly enhance emotion measurement accuracy. Research demonstrates that this approach optimizes mental health monitoring processes, supports clinical decisions, and enables simultaneous monitoring of multiple patients. The technology exhibits substantial clinical value and is expected to promote digital transformation and personalized healthcare services.

This study examined recognition of facial expressions of emotion among women diagnosed with borderline personality disorder (BPD; n = 21), compared to a group of women with histories of childhood sexual abuse with no current or prior diagnosis of BPD (n = 21) and a group of women with no history of sexual abuse or BPD (n = 20). Facial recognition was assessed by a slide set developed by Ekman and Matsumoto (Japanese and Caucasian Facial Expressions of Emotion and Neutral Faces, 1992), expanded and improved from previous slide sets, and utilized a coding system that allowed for free responses rather than the more typical fixed-response format. Results indicated that borderline individuals were primarily accurate perceivers of others' emotions and showed a tendency toward heightened sensitivity on recognition of fear, specifically. Results are discussed in terms of emotional appraisal ability and emotion dysregulation among individuals with BPD.

Puerto Rico Psychiatrists Prepare for MH Crisis

A substantial and growing percentage of older adults misuse alcohol, prescription drugs, or other substances. The number of older adults in need of substance abuse treatment is expected to more than double from 1.7 million in 2000 and 2001 to 4.4 million in 2020. One in four older adults has a significant mental disorder. Among the most common mental health problems in older persons are depression, anxiety disorders, and dementia (Bartels, Blow, Brockmann, & Van Citters, 2005). Saint Vincent Catholic Medical Centers' (New York) Department of Community Medicine operated a comprehensive ambulatory substance abuse/mental health program at the Barbara S. Kleiman (BSK) shelter for homeless men in New York from October 2006 through May 2010. This shelter was the site of the described study that was approved by the New York City Department of Homeless Services (DHS). The analyses and interpretations expressed herein do not necessarily reflect the opinions of DHS or its staff. The study concerns homeless men of all ages but the relevance to older homeless persons is evident. This is particularly so when it is recognized that chronic homelessness causes marked decline in age of survival. At BSK, shelter care, medical care, substance abuse, and mental health care were integrated in a single setting-a large urban homeless shelter. By using a continuum of addiction and mental health services, it promoted a flexible, supportive, and noncoercive environment for clients. We used the principles set forth in the Improvement Protocol (TIP) Series 35: Enhancing Motivation for Change in Substance Abuse Treatment as our evidence-based practice. We were able to incorporate the transtheoretical model of change that facilitated positive outcomes in the patient population. Other elements used in our daily activities included individual counseling, small group interactions, and facility or community-level interventions. Little or no adaptation/ modification was necessary to create the proposed treatment initiative at BSK. The project's goal was for clients to achieve sobriety and a positive drug-free social life, develop life skills and educational/ vocational training, and obtain and maintain independent living and employment. TARGET POPULATION Prior to full program implementation, a focus group was conducted using prospective candidates who met our admissions criteria. These persons were referred to us by the staff at the New York City DHS. The results of the focus group indicated that 53.6% would be Black or African American, 1.8% Asian, 26.8% White, 3.6% multiracial, and 14.2% from other racial population. By ethnicity, 21.4% self-reported to be Hispanic. Alcohol would be the primary drug of choice (46.4%); marijuana (19.6%); cocaine/crack (14.3%); heroin (5.4%); other opiates including benzodiazepines, percocet, and codeine (9.0%). The men averaged at least 6-12 months of homelessness each year. New York City was the target population's service area. RECRUITMENT Recruitment of clients for the program evaluation was done via the DHS assessment shelter system. Employed or employable clients with a reported substance abuse and/or mental health condition were referred to BSK. At intake, they received a biopsychosocial, psychiatric, comprehensive medical, and a drug screen using Drug Abuse Screening Test (DAST-10). A BSK case manager assessed clients for other unmet needs. INCLUSION CRITERIA All persons were admitted who, based on a thorough clinical evaluation, were judged to benefit from ongoing treatment for substance abuse in a program of several weekly group treatments. EXCLUSION CRITERIA Potential clients without a Diagnostic and Statistical Manual of Mental Disorders 4th Edition Text Review (DSM IV-TR) Axis I diagnosis of a substance abuse disorder or both a substance abuse and a psychiatric disorder were excluded. Target Enrollment Number targeted for enrollment (5-year target): 390. …

Due to the disorderly access to medical care and inefficient use of health resources, the advancement of the hierarchical diagnosis and treatment is more valued in promoting health system reform. Hence, this article integrates prospect theory into an evolutionary game model of the local government health departments, the medical institutions, and the patients in the system promotion of the hierarchical diagnosis and treatment. The simulation shows the specific influencing mechanism of the psychological perceived value of game subjects. Then by introducing the stochastic evolutionary game model, the system promotion under different medical cultures is also discussed in detail. The results indicate that for local government health departments, the amount and duration of financial subsidies are the key factors influencing the game system’s evolution. For medical institutions, participating in the hierarchical diagnosis and treatment system is relatively beneficial. For patients, the recovery rate in primary hospitals matters more than the cost of treatment. Changes in the risk sensitivity coefficient will cause the equilibrium of the game system to change. However, changes in the loss avoidance factor do not change the equilibrium and only have an impact on the speed of convergence. With the health departments’ intervention, patients in rural medical culture are more inclined to support the hierarchical diagnosis and treatment system than those in urban or town medical culture. Therefore, in order to promote the hierarchical diagnosis and treatment system, this article recommends that more attention should be paid to the regulatory role of health departments and the participation improvement of medical institutions and patients.